Predictive modeling:

A Guided Research Program

About

Can your computer predict what happens next?

Predictive analytics is a rapidly developing field within artificial intelligence and data science that uses historical data, statistical methods, algorithms, and computational models to estimate the likelihood of future outcomes.

This apprenticeship pairs you with a mentor specialising in Artificial Intelligence and Machine Learning to develop a hands-on project in predictive modelling. You will explore how predictive models are built, trained, and evaluated, learning to work with data and algorithms to investigate real-world questions and make data-informed forecasts.

Learn More

Mentors

Nwanacho Nwana | Lead mentor

Indipendent Researcher and Author | Massachusetts Institute of Technology

Aditi Palit | Research Supervisor

Doctoral Candidate | Indian Institute of Technology

Schedule

Start Date

November 21, 2026

Group Lectures

11.00 AM IST every Saturday

Office Hours

25 hours of bookable one-on-one time with a mentor to work on a research paper.

Duration

Four months

artwork: Walzendruckpapier mit Golddruck (dichtes florales Muster) (1855 - 1900)

All selected candidates to this course are provided with a one-year membership to JSTOR through the ITHAKA access initiative.

Prerequisites

Students are expected to have a foundational background in computer science, along with a working knowledge of Python and its core libraries (such as NumPy and pandas). A basic understanding of probability theory and statistics is essential, as the course builds on these concepts to develop and evaluate predictive models.

Who This Is For

This course is designed for students who are curious about:

Statistics, probability, and data science
Economics, forecasting, and prediction markets
How platforms like Kalshi, Polymarket, or ESPN Analytics operate
Building systems that go live and generate real-world signals—not just static reports
Conducting research that can be published, cited, or extended into future academic or professional work

Project Focus:

In the duration of the course, you are required to. Select any one market to create prediction models for:

Football, Basketball, Cricket, Tennis, Weather Patterns, Political Events among a range of pattern-based data sources to choose from.

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Nwanacho Nwana | Lead Mentor

Nwanacho Nwana is an educator, researcher, and writer who currently teaches mathematics at the District of Columbia International School (DCI). He is deeply passionate about leveraging data and AI to address educational challenges, particularly in public speaking anxiety and learning equity. As co-founder and Head of Product at Valfee, he has built an app-based platform that uses AI to help students and young entrepreneurs improve through mentorship, deliberate practice, and observational learning. He is a strong advocate for neurodivergent and underrepresented students and integrates his scholarly approach with social impact

Aditi Palit | Research Supervisor

She is a fourth-year PhD candidate in Computer Science at IIT Tirupati, specialising in federated learning, privacy-preserving algorithms, and decentralised data processing. She has research experience with Adobe and Ericsson, focusing on diffusion models, synthetic data strategies, and federated learning optimisation.

Candiates will be provided with 25 hours of one-on-one time to draft their research project.

Week 1 | November 21 | Saturday

Finding and Prioritizing Data Sources

Every prediction starts with a question and the evidence available to answer it. Learn how to identify relevant data sources, assess their usefulness, and prioritize the information most likely to improve a forecast.

Week 2 | November 28 | Saturday

Preparing Data for Analysis

Raw data is rarely ready to use. Learn how to clean, organize, and structure datasets so that they can be used to build forecasts based on information that would actually have been available at the time.

week 3 | December 5 | Saturday

Building Your First Forecast: Base Rates and Baselines

Before building complex models, learn how to make predictions using historical patterns and simple reference points. Establish a baseline to determine whether a more sophisticated model actually improves your forecasts.

Week 4 | December 12 | Saturday

Choosing the Right Predictive Model

Different problems call for different approaches. Explore how data availability, problem structure, and validation results can guide model selection and help you choose an approach that performs reliably.

week 5 | December 26 | Saturday

Understanding and Interpreting Models

A prediction is only useful if you understand what informs it. Learn how to interpret model outputs, identify the factors influencing predictions, and recognize when a model's results may be misleading or unreliable.

How do you know whether a prediction is good? Learn how probabilistic forecasts are scored, how to compare predictions against actual outcomes, and why a reliable forecasting method must be evaluated across many predictions and not just one.

Week 6 | January 2, 2027| Saturday

Measuring Forecast Accuracy

WEek 7 | January 9, 2027 | Saturday

Testing Models and Understanding Uncertainty

A model that performs well on past data may not perform as well in the future. Learn how to validate forecasts, recognize overfitting and the risks of misleading backtests, and account for uncertainty when interpreting results.

Week 8 | January 16, 2027 | Saturday

Communicating Predictions with Data

Turn your analysis into insights others can use. Learn how to present forecasts through clear visualizations, explain your findings, communicate uncertainty, and show how predictive models can inform real-world decisions.

A dedicated month of interactive office hours where participants can receive personalized guidance, discuss challenges, ask questions, and get feedback on their ongoing projects. These sessions will help participants strengthen their models, address technical gaps, and refine their approach ahead of the final presentations.

January 23 to February 13 | Saturdays

Group Coding Sessions

Final Presentation + forecasting Competition
What we’ll do

The final has two parts judged together: an 8-minute presentation with 4 minutes of Q&A, and your live forecast record scored against the market. Fellows presenting from different time zones may submit a recorded talk and join live for Q&A.

Awards
Fellows are recognized in three categories at the final competition. Each rewards a different strength, so a fellow can do well in one even without winning another.

Best Overall Project: highest total on the judging rubric

Best Forecaster: highest live skill score against the market

Best Communicator: highest communication score

Real Research

This course build's on Kuhiro Class' Basketball IQ project which has already answered this question for college basketball: yes. Across 1,544 games, the Basketball IQ model outperformed Kalshi's predictions by 1.5 percentage points (70.1% vs 68.6%).

That result is time-stamped, walk-forward validated, and publicly verifiable. The gap we are now trying to close is coverage. One sport has been proven. Twelve more are waiting. That is the research mandate for this program.

Student Evaluation & Certification

Students will receive a letter of engagement upon completing the project, and mentors will evaluate their performance to issue an end-of-project recommendation letter where appropriate. Throughout the program, mentors will assess each student’s engagement during group sessions, the quality and timeliness of their assigned work, and their creativity in developing solutions to group tasks.

Patent and Copyrights

All intellectual property, copyrights, and potential patents arising from this project will be owned solely by the mentor. Distribution of any ownership rights, authorship credit, or derivative use permissions to participating group members will be at the mentor’s discretion and may be allocated based on class performance, contribution level, and the overall quality of engagement throughout the project.

Predictive modeling is one of the most important skills in the modern era. With a plethora of data sources, and many accessible AI tools, the ability to choose the correct data and build the best models to predict outcomes will be essential to every field. In this fellowship, you will not only learn the math behind good models, but how to think critically about what data matters and what strategy will yield to most stable and accurate predictions in any field.

Featuring

Guided Mentorship

Learn through interactive sessions and discussions led by Mrs Ritika Nara Murtagh, with opportunities to schedule one-on-one conversations to ask questions, discuss research ideas, and receive personalized guidance based on your interests.

Publication Support

Take your research beyond the apprenticeship! Receive guidance on developing your work into a research paper, from exploring the literature and shaping your ideas to preparing your work for potential publication.

Kuhiro Class Credits

All selected apprentices receive 30 "credits" to use across the Kuhiro Class platform, connecting with mentors from around the world for support with college applications or research papers.

An Expert Network

Meet other students who are genuinely curious about cancer research, science, and learning. Exchange ideas, build connections, and potentially find your next research collaborator or research buddy along the way!

Applications

Application Deadline: November 12, 2026
Scholarship Form Deadline: November 10, 2026

Sign up for this course by using the registration link below. Seats are limited to 6- 8 and are gauged based on students' past performance.

Application Form

Please send any questions to library@kuhiroclass.com

Tuition Fee

For SAARC Students: INR 1,10,000

For Nepalese Students: NPR 86,000

For International Students: USD 1500

Scholarship Form

All applications are need-blind and do not affect your candidacy to the program. Regional or country background are not disclosed to our selection committee.

We take you to smart places

Over the past four years, Kuhiro Class candidates have conducted research, applied to universities, and participated in workshops in some of the world's most inspiring locations.